Self-supervised Representation Learning for Cell Event Recognition through Time Arrow Prediction
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915011985670144 |
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| author | Chen, Cangxiong Namboodiri, Vinay P. Sero, Julia E. |
| author_facet | Chen, Cangxiong Namboodiri, Vinay P. Sero, Julia E. |
| contents | The spatio-temporal nature of live-cell microscopy data poses challenges in the analysis of cell states which is fundamental in bioimaging. Deep-learning based segmentation or tracking methods rely on large amount of high quality annotations to work effectively. In this work, we explore an alternative solution: using feature maps obtained from self-supervised representation learning (SSRL) on time arrow prediction (TAP) for the downstream supervised task of cell event recognition. We demonstrate through extensive experiments and analysis that this approach can achieve better performance with limited annotation compared to models trained from end to end using fully supervised approach. Our analysis also provides insight into applications of the SSRL using TAP in live-cell microscopy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03924 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-supervised Representation Learning for Cell Event Recognition through Time Arrow Prediction Chen, Cangxiong Namboodiri, Vinay P. Sero, Julia E. Computer Vision and Pattern Recognition The spatio-temporal nature of live-cell microscopy data poses challenges in the analysis of cell states which is fundamental in bioimaging. Deep-learning based segmentation or tracking methods rely on large amount of high quality annotations to work effectively. In this work, we explore an alternative solution: using feature maps obtained from self-supervised representation learning (SSRL) on time arrow prediction (TAP) for the downstream supervised task of cell event recognition. We demonstrate through extensive experiments and analysis that this approach can achieve better performance with limited annotation compared to models trained from end to end using fully supervised approach. Our analysis also provides insight into applications of the SSRL using TAP in live-cell microscopy. |
| title | Self-supervised Representation Learning for Cell Event Recognition through Time Arrow Prediction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.03924 |